Chapter 1:<\/b><\/p>\n
Concepts & Definitions<\/span><\/i><\/p>\n Notes<\/span><\/p>\n Questions<\/span><\/i><\/p>\n <\/p>\n Chapter 2:<\/span><\/p>\n Concepts & Definitions<\/span><\/i><\/p>\n Notes<\/span><\/p>\n Questions<\/span><\/i><\/p>\n <\/p>\n Chapter 3:<\/span><\/p>\n Concepts & Definitions<\/span><\/i><\/p>\n Notes<\/span><\/p>\n Chapter 1: Concepts & Definitions GIS Analysis: looking at spatial data to identify patterns and relationships Geographic Features: Discrete feature = exact (roads); Continuous phenomena = measurable everywhere (temp); Summarized by area = counts or an aggregation (population per country) Data Models: Vector = points, lines, x,y coords in tables; Raster = grid\/cells, each has a value (continuous data) Map projections & coord systems: projection = going from curved surface to flat map; coord system = defines measurement units and origin for locations Geographic Attributes = descriptive info tied to features; Categories = groups features (crime type); Ranks = order features by value; Counts = number of features; Amounts = measurable quantity; Ratios = relationships between quantities Continuous and Noncontinuous values: noncontinuous = fixed set values; continuous = any value in a range For Data tables: Select by using queries to filter data; use =,<,>; calculating by adding new fields or computing values; summarizing by getting totals, averages, and frequencies Notes GIS can be used for data exploration and is not just cartography Framing the right questions is highly important, along with the analysis Data tables seem to be the backbone of GIS analysis How specific you need to be depends on what data you are trying to collect Reading this text, while illuminating, doesn\u2019t fully give me an idea of how to map, sadly Chapter lays the foundations: features, attributes, models, projections Need good questions, data, and choices for a good GIS map Fundamentals of GIS have remained the same despite technology advancing rapidly Knowing your audience is important, casual versus scientific versus legal contexts Two similar maps can answer completely different questions depending on the data used GIS can be used for infrastructure planning Questions What does GIS actually look like? How do you factor error into your data on GIS? How many layers can you add to a map? How friendly are the tools to a newcomer? Chapter 2: Concepts & Definitions Category values = feature that has a code that identifies its type, like whether a crime is a homicide or theft General code for attributes is the major type and detailed code is the sub type Single type map = all features use same symbol (very basic) Grouping categories = multiple categories grouped together to make patterns easier to view; instead of 1. Heavy industrial, 2. Light industrial, 3. Medium industrial, group to just Industrial Notes Maps used to see where or what an individual feature is Patterns help to better understand an area while mapping Locations and features can allow you to see patterns For geographic patterns in data, mapping features in a layer using different kinds of symbols is ideal If an audience is unfamiliar with an area\/data shown on map, use information that will provide reference locations, like roads or lakes GIS reads location information or latitude and longitude values and assigns geographic coordinates Many categories are hierarchical, state highways into how heavy traffic is on them GIS can use coordinate pairs to define the location of an address (4 points of a square) GIS can be used to map a subset of the data; all crimes into just selecting only jaywalking, which can reveal patterns Mapping subsets most common for individual locations Map showing only subsets of features could be incomplete Can change the color and symbols\/characteristics of categories Features might belong to more than one category If patterns complex or features close together, creating a separate map for each category can make patterns easier to view If showing several categories on one map, display no more than seven categories When smaller areas mapped, individual features easier to see so using not enough categories can leave information out The way categories grouped or changed influence the perception of information Can group categories by using a general code to \u2018combine\u2019 them or by using two tables with the detailed codes corresponding to a general code Text labels can help identify categories Landmarks always helpful for people Zooming in and out can reveal patterns, like clusters Patterns may be the result of a multitude of factors, so statistics to measure the relationship between these features is important Questions Can you use any shape or symbol for categories? How hard is it to specify a location using points? Chapter 3: Concepts & Definitions Continuous phenomena = defined areas or a surface of continuous values Data summarized = amount of category in each area Counts = actual number of features on the map Amount = total value associated with each feature Ratios = relationship between two quantities; averages, proportions (%), densities Densities = where features concentrated; ex: population of a city \/ land area (Sq Mi), people per square mile Ranks = putting features in order from highest to lowest Classification schemes = grouping similar values to look for patterns in data; may want map to focus more on highest income households or focus more on the number of classes; four common schemes = natural breaks, quantile, equal interval, and standard deviation Z-factor =\u00a0 a value that increases variation in the surface for 3D Notes Mapping features based on quantities can add additional levels of information beyond just a location, like amount of customers at a shop instead of shops with customers Make sure to keep the purpose of your map and audience in mind; exploring data versus showing a map Knowing the type of quantities being mapped is the best way to showcase the data Counts and amounts can skew patterns if areas vary in size, using ratios or percentages can be more accurate to represent features Proportions great to show what part of a whole you want a quantity to represent Ratio = 1\/10 versus percent = 1\/10 * 100 Can create ratios by adding an extra field in the layer\u2019s data table ArcGIS lets you create them by setting up the calculation Ranks useful for direct measurement; may rank suitability for growing crops; 1-10 Block groups can show off data values using shades Mapping individual values may give an accurate showcase of the data but is more time consuming, so ranks may be better for your sanity Each classification scheme has pro\u2019s and con\u2019s, just depending on what you want the map to showcase, creating a bar chart can help If outliers, using natural breaks can help isolate them If trying to use shades to showcase different percent’s, use up to seven colors on a map Page 93 of chapter 3 good resource for what map you wanna make Can create pie charts on graduate symbols<\/p>\n","protected":false},"author":2322,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-5021","post","type-post","status-publish","format-standard","hentry","category-course-student-work"],"_links":{"self":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/5021","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/users\/2322"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=5021"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/5021\/revisions"}],"predecessor-version":[{"id":5022,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/5021\/revisions\/5022"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=5021"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=5021"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=5021"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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